Fine-tuning Large Language Models for Automated Diagnostic Screening Summaries

arXiv:2403.20145v1 Announce Type: new
Abstract: Improving mental health support in developing countries is a pressing need. One potential solution is the development of scalable, automated systems to conduct diagnostic screenings, which could help alleviate the burden on mental health professionals. In this work, we evaluate several state-of-the-art Large Language Models (LLMs), with and without fine-tuning, on our custom dataset for generating concise summaries from mental state examinations. We rigorously evaluate four different models for summary generation using established ROUGE metrics …

abstract art arxiv automated cs.cl developing countries development diagnostic fine-tuning health improving language language models large language large language models llms mental health professionals scalable screening solution state support systems type work